Spatiotemporal Evolution and Drivers of the Carbon Footprint and Embodied Carbon Transfer in the Advanced Manufacturing Industry: Case Study of the Western Region in China

Abstract

Motivated by the policy urgency of China’s dual-carbon goals and the practical obstacle that official input–output (IO) and MRIO tables are sparse and non-consecutive, this study investigates how to generate credible, mechanism-aware provincial–sector forecasts of carbon footprints and embodied transfers for Western China—a region with pronounced structural heterogeneity. We develop a regionalized forecasting pipeline that fuses balance-constrained MRIO completion (RAS–CE) with a Whale-optimized Grey Neural Network (WOA–GNN), bridging the data gap (2007–2017 reconstruction) and delivering 2018–2030 projections at province–sector resolution. The novelty lies in integrating RAS–CE with a meta-heuristic grey learner and layering explainable network analytics—Grey Relational Analysis (GRA) for factor ranking, complex-network measures with QAP regressions for driver identification, and SHAP for post hoc interpretation—so forecasts are not only accurate but also actionable. Empirically, (i) energy mix/intensity and output scale are the dominant amplifiers of footprints, while technology upgrading (process efficiency, electrification) is the most robust mitigator; (ii) a structural sectoral hierarchy persists—S2 (non-metallic minerals) remains clinker/heat-intensive, S3 (general/special equipment) operates as a mid-chain hub, and S6/S7 (electrical machinery/instruments) maintain lower, more controllable intensities as the grid decarbonizes; (iii) by 2030, the embodied carbon network becomes denser and more centralized, with Sichuan–Chongqing–Guizhou–Guangxi forming high-betweenness corridors; and (iv) QAP/SHAP converge on geographic contiguity (D) and economic differentials (E) as the strongest positive drivers (openness Z and technology gaps T secondary; energy-mix differentials F weakly dampening). Policy-wise, the framework points to green-power contracting and trading for hubs, deep retrofits in S2/S3 (low-clinker binders, waste-heat recovery, efficient drives, targeted CCUS), technology diffusion to lagging provinces, and corridor-level governance—demonstrating why the RAS–CE + WOA–GNN coupling is both necessary and impactful for data-constrained regional carbon planning.